Apparatus and method for breathing pattern determination using a non-contact microphone

ABSTRACT

A method is provided for analyzing respiration of a subject ( 20 ). Using a non-contact microphone ( 22 ), a raw signal indicative of airflow sounds of the respiration is generated. The raw signal is analyzed to determine a first set of one or more parameters of the respiration. An algorithm is applied to the first set of parameters to derive a second set of one or more estimated parameters of the respiration that are not generally directly measurable in the raw signal. Other embodiments are also described.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.11/572,483, filed May 31 2007, which is the U.S. National Phase ofPCT/IL2005/000778 filed Jul. 21, 2005, which claims the benefit of U.S.Provisional Patent Application Ser. No. 60/590,508, filed Jul. 23, 2004,which is assigned to the assignee of the present application and isincorporated herein by reference. The International Application waspublished on Jan. 26, 2006 as WO 2006/008745 A2 under PCT Article 21(2).

FIELD OF THE INVENTION

The present invention relates generally to medical devices, andspecifically to devices that monitor and/or modify biorhythmic activityof a user.

BACKGROUND OF THE INVENTION

Physical exercise often involves modifying a multi-phase biorhythmicactivity, such as breathing. Breathing patterns display irregularitiesin a number of cardiovascular diseases, including congestive heartfailure (CHF), and pulmonary diseases, including chronic obstructivepulmonary disease (COPD). These irregularities are known markers fordisease-related mortality and morbidity. Typical irregularities includeCheyne-Stokes breathing (recurrent episodes of central apnea alternatingwith hyperpnea), amplitude-modulated breathing (periodic breathing) at arate of about one modulation per minute, repeated sighs, and breathingat random amplitudes and periods. A reduction in breathing patternirregularity indicates an improvement in health. The impairment ofcardiovascular reflexes, which control blood pressure and volume in anattempt to minimize fluctuations in blood supply to organs, is alsoclinically significant in cardiovascular and psychosomatic diseases.

U.S. Pat. Nos. 5,076,281, 5,800,337, and 6,090,037 to Gavish, which areincorporated herein by reference, describe methods and devices formodifying biorhythmic activity by measuring one or more variables of auser. The patents describe the generation of a stimulus, which isprovided to the user, so as to change the biorhythmic activity of a userin a way that relates in a predetermined way to the monitoredbiorhythmic activity. The '037 additionally describes a respirationsensor.

U.S. Pat. No. 5,423,328 to Gavish, which is incorporated herein byreference, describes a stress-detecting device for monitoringrespiration, and, in particular, a method for detecting and monitoringcircumferential changes in the chest or abdomen of a user resulting frombreathing.

U.S. Pat. No. 6,662,032 to Gavish et al., which is incorporated hereinby reference, describes techniques for facilitating improving health ofa user, including a first sensor, adapted to measure a firstphysiological variable, which is indicative of a voluntary action of theuser; a second sensor, adapted to measure a second physiologicalvariable, which is not entirely under the direct voluntary control ofthe user; and circuitry, adapted to receive respective first and secondsensor signals from the first and second sensors, and responsivethereto, to generate an output signal which directs the user to modify aparameter of the voluntary action.

U.S. Patent Application Publication 2004/0116784 to Gavish, which isincorporated herein by reference, describes apparatus including asensor, adapted to generate a sensor signal indicative of biorhythmicactivity of a user of the apparatus, the sensor signal having a firstcharacteristic, indicative of a voluntary action of the user, and asecond characteristic, indicative of a benefit-related variable of theuser.

PCT Publication WO 04/014226 to Gavish, which is incorporated herein byreference, describes apparatus including a memory for storing a set ofcomputer instructions, the memory adapted to have stored therein aninitial form of a multi-phase biorhythmic activity pattern and anindication of a desired form of the multi-phase biorhythmic activitypattern, wherein a ratio of durations of two phases in the desired formis different from a ratio of durations of the respective phases in theinitial form, and wherein at least one phase of the multi-phasebiorhythmic activity pattern corresponds to a respective phase of amulti-phase biorhythmic activity of the subject.

Intercure, Inc. (Fort Lee, N.J., USA and Lod, Israel) marketsRESPeRATE™, a device that utilizes some of the techniques described inthe above-referenced patents and patent application publications. Thisdevice for modifying biorhythmic activity includes an input for therespiration signal, a central processing unit, memory, a soundsynthesizing chip, and output to earphones.

U.S. Pat. No. 5,734,090 to Koppel et. al., which is incorporated hereinby reference, describes a method and apparatus for verifying anexpiratory breath flow (e.g., for determining a degree of alcohol in thebreath), utilizing the sonic characteristics of a standardized breath asa reference.

U.S. Pat. No. 6,726,636 to Der Ghazarian et al., which is incorporatedherein by reference, describes a voice recognition breathalyzercomprising a microphone for transducing spoken expression intoelectronic signals and a breathalyzer sensor for transducing a givenbreath content into electronic signals.

U.S. Pat. No. 5,509,414 to Hok, which is incorporated herein byreference, describes techniques for detecting air flow at the mouth andnose of a subject, including a transducer for converting electricalsignals into ultrasound waves and vice versa, means for directing theultrasound waves toward the mouth and nose of the subject and receivingreturn waves, and a detector to analyze electrical signals converted bythe transducer from the return ultrasound waves.

U.S. Pat. No. 5,195,528 to Hok, which is incorporated herein byreference, describes an acoustic respiration detector including at leasttwo tubular air transmission lines having ends which are connected tomicrophone elements. Close to the other ends of the lines are openingsat which turbulence, and hence acoustic signals, are created by theincidence of airflow caused by respiration. A holding element securesthe openings relative to the mouth or nose of a patient whoserespiratory function is to be monitored, and a flow-directing element,for example formed like a face mask, directs the airflow to theopenings. The microphone elements are connected in a bridge circuit withtwo voltage supplying leads and at least one signal lead. Thisarrangement is described as suppressing sensitivity to mechanical andacoustic disturbances.

U.S. Pat. No. 5,797,852 to Karakasoglu et al., which is incorporatedherein by reference, describes sleep apnea screening and/or detectionapparatus for use by a patient breathing through the nose and/or mouthand producing an air flow into and out of the lungs of the patient andcreating audible sounds.

U.S. Pat. No. 6,150,941 to Geiger et al., which is incorporated hereinby reference, describes a stand-off, non-invasive acoustic detector formonitoring physical activity and/or breathing activity of children andinfants.

U.S. Pat. No. 6,261,238 to Gavriely, which is incorporated herein byreference, describes a method for analyzing breath sounds produced by arespiratory system, the method comprising: measuring breath soundsproduced by the respiratory system; tentatively identifying a signal asbeing caused by a breath sound of a given type if it meets a firstcriteria characteristic of the breath sound of the given type; andconfirming said identification if a tentatively identified signal meetsa second criteria characteristic of the breath sound of the given type.

The following patents, all of which are incorporated herein byreference, may be of interest:

U.S. Pat. No. 4,195,626 to Schweizer

U.S. Pat. No. 5,678,571 to Brown

U.S. Pat. No. 5,596,994 to Bro

U.S. Pat. No. 4,883,067 to Knispel et al.

U.S. Pat. No. 4,798,538 to Yagi

U.S. Pat. No. 5,827,179 to Lichter et al.

U.S. Pat. No. 6,001,065 to DeVito

U.S. Pat. No. 5,921,890 to Miley

U.S. Pat. No. 5,027,686 to Ishikawa

U.S. Pat. No. 6,212,135 to Schreiber

U.S. Pat. No. 4,711,585 to Fresquez et al.

The following articles, all of which are incorporated herein byreference, may be of interest:

Cooke et al., “Controlled breathing protocols probe human autonomiccardiovascular rhythms,” American Journal of Physiology 274:H709-H718(1998)

Pitzalis et al., “Effect of respiratory rate on the relationship betweenRR interval and systolic blood pressure fluctuations: afrequency-dependent phenomenon,” Cardiovascular Research 38:332-339(1998)

Bernardi et al., “Effect of breathing rate on oxygen saturation andexercise performance in chronic heart failure,” The Lancet 351:1308-1311(1998)

Mortara et al., “Abnormal awake respiratory patterns are common inchronic heart failure and may prevent evaluation of autonomic tone bymeasures of heart rate variability,” Circulation 96:246-252 (1997)

La Rovere et al., “Baroreflex sensitivity and heart-rate variability inprediction of total cardiac mortality after myocardial infarction,” TheLancet 351:478-484 (1998)

SUMMARY OF THE INVENTION

In some embodiments of the present invention, a method is provided fordetermining a set of parameters of a breathing pattern of a user using astandard non-contact microphone. The method comprises using themicrophone to measure a first subset of one or more parameters ofrespiration by the user, and applying an algorithm to the first subsetof parameters in order to derive a second subset of one or moreestimated parameters of the respiration that are not generally directlymeasurable using the microphone. The second subset of parameters istypically not directly measurable using the microphone because soundsassociated with the second subset, if any, cannot be detected by themicrophone and/or distinguished from background noise. For someapplications, the algorithm is applied by setting the second subset ofestimated parameters equal to a phenomenological function of the firstset of parameters.

Typically, the first subset of parameters includes active expirationtime (i.e., duration of active expiration) and breathing period (i.e.,time between successive breaths), and the second subset of parametersincludes inspiration time. Active expiration time is typically measuredby detecting low-frequency sounds generated by expiratory airflow. Themethod enables the determination of inspiration time, which generallycannot be directly measured using a standard non-contact microphone, atleast in part because inspiratory airflow is too quiet to be detectedunder normal background noise conditions.

In some embodiments of the present invention, a method is provided forimproving biorhythmic signal detection in the presence of potentiallyvariable background signal noise. The method comprises calibrating oneor more signal detection parameters by guiding a user through aplurality of biorhythmic activity phases, and measuring the biorhythmicsignal during the plurality of phases. The signal detection parameterstypically include a signal-to-noise ratio and/or filteringcharacteristics. The signal detection parameters are used for filteringbackground noise from detected biorhythmic signals.

In some embodiments, the biorhythmic activity includes respiration,which is detected using a standard non-contact microphone. The user isguided to inhale for a certain period of time, exhale for a certainperiod of time, and, optionally, to hold his breath for a certain periodof time. The signal detection parameters are typically calibrated by (a)determining signal characteristics of the background noise plus thesounds of inhalation, and the background noise plus the sounds ofexhalation, and (b) using these signal characteristics to determine netbackground noise, and/or signal characteristics of the sounds ofexhalation. Such signal characteristics of the sounds of exhalation aretypically used to determine one or more of the signal detectionparameters. For some applications, this method is used in conjunctionwith techniques for breathing pattern modification, such as thosedescribed in the above-mentioned patents and patent applicationpublications to Gavish and Gavish et al.

These techniques for improving respiration signal detection enable thedetermination of specific signal detection parameters for each userunder specific conditions of use. Such parameters typically vary fromuser to user based on the user's individual breathing habits (such aswhether the user inspires and expires from the nose or from the mouth,and whether the user closes his lips during inspiration and expiration).These techniques enable dynamic determination of signal detectionparameters during daily life in noisy environments.

For some applications, an algorithm is implemented for evaluating one ormore parameters of a non-expiratory portion of the signal substantiallycontinuously. For example, such parameters may include a duration or anamplitude of the non-expiratory portion. In an embodiment, theseparameters are determined responsively to a physiological constraint towhich most people typically adhere. One such constraint reflects thebody's natural tendency to keep ventilation generally constant whileminimizing energy expended. A practical example of this constraint isthat following a relatively deep breath (which transiently increasesventilation), a subsequent breath is typically delayed.

In some embodiments of the present invention, the techniques describedherein are implemented using the standard non-contact microphone of aconventional consumer electronics device, such as a telephone, cellulartelephone, personal digital assistant (PDA), or portable digital audioplayer, which a user may have already purchased for other purposes. Fora cellular telephone, for example, the user may speak directly into themicrophone incorporated into the body of the phone, or, alternatively,the user may speak into an external microphone which plugs into the bodyof the phone. In general, a microphone may be used which is incorporatedinto the body of the consumer electronics device, or which is attachedto the body of the consumer electronics device (e.g., by wire orwirelessly). Typically, the techniques described herein are implementedin software that is installed in such a device, and/or in a centrallocation that is accessed by the device over a conventional wireless orwired network.

In some embodiments of the present invention, the techniques describedherein are implemented using a non-contact microphone that is integratedinto a medical device in fluid communication (e.g., via air or anothergas) with respiration-related airflow of the subject. For example, themedical device may comprise a breathing mask or a tube, such as atracheotomy tube.

For some applications, the breathing mask or tube are components of aventilator that applies positive pressure to the lungs of the subject.The techniques described herein are used to detect proper performance ofthe ventilator, typically by detecting active expiration by the subject.Active expiration is typically measured by detecting low-frequencysounds indicative of expiratory airflow, rather than by detecting soundsof breathing. (In such subject, sounds of breathing often do notcorrelate with expiration, because the sounds of breathing are oftenaffected by various constrictions in the subject's airways. However,low-frequency sounds indicative of expiratory airflow are not generallyaffected by such constrictions.) For some applications, the techniquesdescribed herein are used for non-contact monitoring of breathing duringweaning from ventilation.

In an embodiment of the present invention, the techniques describedherein are used for non-contact monitoring of breathing during use of adrug inhaler by the subject. For some applications, such non-contactmonitoring of breathing is combined with techniques for modifyingbreathing activity of the subject, such as those described in theabove-mentioned patent and patent application publications to Gavish andGavish et al.

In an embodiment of the present invention, the techniques describedherein and/or in the above-mentioned patent and patent applicationpublications to Gavish and Gavish et al. are used to treat a subjectsuffering from insomnia. Insomnia is sometimes caused by disorderedbreathing, such as fast and shallow breathing. For some applications,insomnia is treated using techniques described herein for detecting andmonitoring breathing, in combination with techniques for modifyingrespiration-related biorhythmic activity of the subject described in theabove-mentioned patent and patent application publications to Gavish andGavish et al.

In an embodiment of the present invention, the breathing monitoringtechniques described herein are used for detecting sleep-disorderedbreathing, such as sleep-disordered breathing associated with sleepapnea or sudden infant death syndrome (SIDS). Typically,breath-by-breath airflow during exhalation is monitored. For someapplications, such non-contact monitoring of breathing is combined withtechniques for modifying breathing activity of the subject, such asthose described in the above-mentioned patent and patent applicationpublications to Gavish and Gavish et al.

There is therefore provided, in accordance with an embodiment of thepresent invention, a method for analyzing respiration of a subject, themethod including:

using a non-contact microphone, detecting airflow sounds of therespiration, and converting the sounds into a signal;

analyzing the signal to determine a first set of one or more parametersof the respiration; and

applying an algorithm to the first set of parameters to derive a secondset of one or more estimated parameters of the respiration that are notgenerally directly measurable in the signal.

In an embodiment:

the first set of parameters includes an active expiration time and abreathing period of the respiration,

the second set of parameters includes an inspiration time of therespiration,

analyzing the signal includes analyzing the signal to determine theactive expiration time and the breathing period, and

applying the algorithm includes applying the algorithm to derive theinspiration time.

There is further provided, in accordance with an embodiment of thepresent invention, a method for analyzing respiration of a subject, themethod including:

detecting airflow sounds of the respiration, and converting the soundsinto a signal;

guiding the user through a plurality of respiration phases;

analyzing the signal during the guided respiration phases, and definingone or more parameters of a filter responsively to the analysis; and

filtering background noise from the signal using the filter having thedefined parameters.

In an embodiment, guiding the user includes guiding the user throughinspiratory and expiratory respiration phases.

There is also provided, in accordance with an embodiment of the presentinvention, a method for modifying naturally-occurring multi-phasebiorhythmic activity of a subject, the method including:

detecting a signal indicative of the multi-phase biorhythmic activity;

analyzing the signal to determine one or more parameters of a filter;

filtering background noise from the signal using the filter having theparameters;

at least in part responsively to the filtered signal, determining astimulus input which is operative to change at least one aspect of thebiorhythmic activity of the subject; and

providing the stimulus input to the subject.

For some applications, filtering the background noise includes frequencyfiltering the signal. Alternatively or additionally, filtering thebackground noise includes performing non-frequency spectral analysis onthe signal in order to classify the signal according to one or morevariables.

In an embodiment, the background noise is indicative of secondarybiorhythmic activity different from the multi-phase biorhythmicactivity, and filtering the background noise from the signal includesfiltering the secondary biorhythmic activity-related background noisefrom the signal.

In an embodiment, the multi-phase biorhythmic activity includesrespiration of the subject, and detecting the signal includes detectingthe signal indicative of the respiration. For some applications, thebackground noise includes a heartbeat-related component of the signal,and filtering the background noise from the signal includes filteringthe heartbeat-related component from the signal.

In an embodiment, filtering the background noise includes performingspectral analysis on the signal to produce a frequency spectrum. Forsome applications, performing the spectral analysis includes frequencyfiltering the frequency spectrum.

For some applications, filtering the background noise includes removingnon-frequency-related noise from the signal. For some applications, thenon-frequency-related noise includes a heartbeat-related component ofthe signal, and removing the non-frequency-related noise includesremoving the heartbeat-related component of the signal from the signal.

There is further provided, in accordance with an embodiment of thepresent invention, a method for analyzing respiration of a subject, themethod including:

using a non-contact microphone, generating a raw signal indicative ofairflow sounds of the respiration;

analyzing the raw signal to determine a first set of one or moreparameters of the respiration; and

applying an algorithm to the first set of parameters to derive a secondset of one or more estimated parameters of the respiration that are notgenerally directly measurable in the raw signal.

In an embodiment, applying the algorithm includes setting the second setof one or more estimated parameters equal to a phenomenological functionof the first set of one or more parameters.

In an embodiment, the first set of parameters includes a measure ofbreathing amplitude of the respiration, and analyzing the raw signalincludes integrating the airflow sounds for a breath of the respirationto determine the measure of breathing amplitude. For some applications,the first set of parameters includes a measure of breathing amplitude ofthe respiration, the second set of parameters is selected from the listconsisting of: a measure of ventilation of the subject, and a measure ofbreathing irregularity of the subject, and applying the algorithmincludes applying the algorithm to the measure of breathing amplitude toderive the selected second set of parameters.

For some applications, the method includes analyzing at least one of thefirst and second sets of parameters to derive at least one additionalbreathing-related parameter of the subject selected from the listconsisting of: breathing amplitude, a geometrical property of airflow ofthe subject, a characteristic of the airflow indicative of pursed lipsbreathing, a characteristic of the breathing indicative of relaxedbreathing, a characteristic of the breathing indicative of passiveelastic recoil of lungs of the subject, a characteristic of breathingwith effort, and a characteristic of breathing during which the lungs ofthe subject undergo a functional change.

In an embodiment, the method includes guiding the subject to performbreathing in a plurality of respiration phases determined at least inpart responsively to the second set of parameters. For someapplications, guiding the subject to perform the breathing includestreating insomnia of the subject by guiding the subject to perform thebreathing in the plurality of respiration phases.

In an embodiment, the non-contact microphone includes a non-contactmicrophone of a consumer electronics device capable of performing atleast one function that does not facilitate analyzing respiration of thesubject, and generating the raw signal includes using the non-contactmicrophone. In an alternative embodiment, the non-contact microphone isintegrated into a medical device in fluid communication withrespiration-related airflow of the subject, and generating the rawsignal includes using the integrated non-contact microphone. Forexample, the medical device may include a drug inhaler, and generatingthe raw signal includes using the non-contact microphone integrated intothe drug inhaler.

In an embodiment, analyzing the raw signal includes deriving anexpiratory airflow sound signal from the raw signal, and analyzing theexpiratory airflow sound signal to determine the first set ofparameters. For some applications, the method includes generating areal-time indication for the subject that indicates whether expirationhas been detected.

In an embodiment, the first set of parameters includes an activeexpiration time and a breathing period of the subject, and analyzing theexpiratory airflow sound signal includes analyzing the expiratoryairflow sound signal to determine the active expiration time and thebreathing period. For some applications, the second set of parametersincludes an amplitude of a non-expiratory portion of the respiration,and applying the algorithm includes applying the algorithm to derive theamplitude of the non-expiratory portion of the respiration.

In an embodiment, the second set of parameters includes an inspirationtime of the subject, and applying the algorithm includes applying thealgorithm to derive the inspiration time.

In an embodiment, applying the algorithm to derive the inspiration timeincludes determining whether a difference between the breathing periodand the active expiration time is greater than or equal to a firstfunction of the active expiration time, responsively to a positivedetermination, setting the inspiration time equal to a second functionof the difference, and responsively to a negative determination, settingthe inspiration time equal to a third function of the active expirationtime.

For some applications, determining includes determining whether thedifference between the breathing period and the active expiration timeis greater than or equal to the active expiration time.

For some applications, setting responsively to the positivedetermination includes setting the inspiration time equal to a valuewithin plus or minus 20% of the difference, such as within plus or minus10% of the difference. For some applications, the second functionincludes a function of the difference and a phenomenological constant,and setting responsively to the positive determination includes settingthe inspiration time equal to the second function of the difference andthe phenomenological constant. For some applications, settingresponsively to the positive determination includes determining thephenomenological constant at least in part responsively to at least oneparameter of the first set of one or more parameters.

For some applications, setting responsively to the negativedetermination includes setting the inspiration time equal to a valuewithin plus or minus 20% of the active expiration time, such as withinplus or minus 10% of the active expiration time. For some applications,the third function includes a function of the active expiration time anda phenomenological constant, and setting responsively to the negativedetermination includes setting the inspiration time equal to the thirdfunction of the active inspiration time and the phenomenologicalconstant. For some applications, setting responsively to the negativedetermination includes determining the phenomenological constant atleast in part responsively to at least one parameter of the first set ofone or more parameters.

For some applications, applying the algorithm to derive the inspirationtime includes setting the inspiration time equal to a function of adifference between the breathing period and the active expiration time.For some applications, setting the inspiration time includes setting theinspiration time equal to a value within plus or minus 20% of thedifference, such as within plus or minus 10% of the difference. For someapplications, the function includes a function of the difference and aphenomenological constant, and setting the inspiration time includessetting the inspiration time equal to the function of the difference andthe phenomenological constant. For some applications, setting theinspiration time includes determining the phenomenological constant atleast in part responsively to at least one parameter of the first set ofone or more parameters.

For some applications, applying the algorithm to derive the inspirationtime includes setting the inspiration time equal to a function of theactive expiration time. For some applications, setting the inspirationtime includes setting the inspiration time equal to a value within plusor minus 20% of the active expiration time, such as plus or minus 10% ofthe active expiration time. For some applications, the function includesa function of the active expiration time and a phenomenologicalconstant, and setting the inspiration time includes setting theinspiration time equal to the function of the active expiration time andthe phenomenological constant. For some applications, setting theinspiration time includes determining the phenomenological constant atleast in part responsively to at least one parameter of the first set ofone or more parameters.

For some applications, the method includes analyzing the derivedinspiration time to determine an amplitude of breathing during theinspiration time.

In an embodiment, deriving the expiratory airflow signal includes:

digitizing the raw signal to generate a digital signal;

performing spectral analysis on the digital signal to produce afrequency spectrum; and

filtering the frequency spectrum to eliminate frequencies outside of arange of frequencies associated with expiratory airflow sounds.

For some applications, filtering the frequency spectrum includes settingthe range to be between a first frequency and a second frequency, thefirst frequency between 30 and 50 Hz, and the second frequency between100 and 200 Hz.

For some applications, filtering the frequency spectrum includes:

guiding the subject to perform breathing in a plurality of alternatinginspiratory and expiratory respiration phases;

using the non-contact microphone, generating a raw calibration signalindicative of airflow sounds of the respiration during the respirationphases;

digitizing the raw calibration signal to generate a digital calibrationsignal, and performing spectral analysis on the digital calibrationsignal to produce an inspiration frequency spectrum and an expirationfrequency spectrum;

subtracting the inspiration spectrum from the expiration spectrum toobtain a net frequency spectrum;

determining a first frequency and a second frequency by analyzing thenet frequency spectrum; and

setting the range to be between the first and second frequencies.

For some applications, determining the first and second frequenciesincludes:

setting the first frequency such that an area under a first portion ofthe net spectrum having a frequency less than the first frequency isless than a first percentage of a total area under the net spectrum; and

setting the second frequency such that an area under a second portion ofthe net spectrum having a frequency greater than the second frequency isless than a second percentage of the total area under the net spectrum.

In an embodiment, deriving the expiratory airflow sound signal from theraw signal includes filtering the raw signal to eliminate frequenciesoutside of a range of frequencies associated with expiratory airflowsounds. For some applications, filtering the raw signal includes settingthe range to be between a first frequency and a second frequency, thefirst frequency between 30 and 50 Hz, and the second frequency between100 and 200 Hz.

In an embodiment, analyzing the raw signal includes setting a detectionthreshold, and deriving the expiratory airflow sound signal includesinterpreting portions of the raw signal having a signal strength greaterthan the detection threshold as the expiratory airflow sound signal. Forsome applications, setting the detection threshold includes setting thedetection threshold at a level sufficient to reduce erratic peaks in theraw signal that are not associated with functional breathing. For someapplications, setting the detection threshold includes:

digitizing the raw signal to generate a digital signal having flowvalues at respective points in time, and buffering the flow values ofthe digital signal over a period of time;

transforming the buffered flow values into a histogram having aplurality of bins;

designating one of the bins having a greatest number of points as amaximum bin;

selecting two of the bins on opposite sides of the maximum bin;

setting a width of a noise band equal to a flow interval between the twobins; and

setting the detection threshold responsively to a flow value of themaximum bin and the width of the noise band.

For some applications, setting the detection threshold includes settingthe detection threshold equal to the flow value of the maximum bin plusa product of a constant and the width of the noise band.

In an embodiment, the method includes detecting sleep-disorderedbreathing by analyzing at least one parameter selected from: the firstset of parameters, and the second set of estimated parameters. For someapplications, the sleep-disordered breathing includes breathingassociated with apnea, and detecting the sleep-disordered breathingincludes detecting the apnea. For other applications, thesleep-disordered breathing includes breathing associated with suddeninfant death syndrome (SIDS), and detecting the sleep-disorderedbreathing includes detecting the SIDS.

There is still further provided, in accordance with an embodiment of thepresent invention, a method for analyzing respiration of a subject, themethod including:

generating a signal indicative of airflow sounds of the respiration;

guiding the subject to perform breathing in a plurality of respirationphases;

analyzing the signal during the guided respiration phases, and definingone or more parameters of a filter responsively to the analysis; and

filtering background noise from the signal using the filter having thedefined parameters.

In an embodiment, generating the signal includes generating the signalusing a non-contact microphone.

for some applications, the one or more parameters of the filter includea signal-to-noise ratio, and defining the one or more parametersincludes defining the signal-to-noise ratio.

In an embodiment, guiding the subject includes guiding the subject toperform breathing in a plurality of inspiratory and expiratoryrespiration phases. For some applications, guiding the subject includesguiding the subject to perform breathing in inspiratory, expiratory, andbreath-holding respiration phases.

For some applications, defining the one or more parameters of the filterincludes:

determining signal characteristics of background noise plus airflowsounds of inhalation, and of the background noise plus airflow sounds ofexhalation;

determining net background noise responsively to the signalcharacteristics; and

defining the one or more parameters of the filter responsively to thenet background noise.

For some applications, the one or more parameters of the filter includea first frequency and a second frequency, analyzing the signal includes:

-   -   digitizing the signal to generate a digital signal, and        performing spectral analysis on the digital signal to produce an        inspiration frequency spectrum and an expiration frequency        spectrum;    -   subtracting the inspiration spectrum from the expiration        spectrum to obtain a net frequency spectrum; and    -   determining the first and second frequencies by analyzing the        net frequency spectrum, and

filtering the background noise includes eliminating frequencies outsideof a range of frequencies defined by the first and second frequencies.

For some applications, determining the first and second frequenciesincludes:

setting the first frequency such that an area under a first portion ofthe net spectrum having a frequency less than the first frequency isless than a first percentage of a total area under the net spectrum; and

setting the second frequency such that an area under a second portion ofthe net spectrum having a frequency greater than the second frequency isless than a second percentage of the total area under the net spectrum.

There is yet further provided, in accordance with an embodiment of thepresent invention, a method for analyzing respiration of a subject, themethod including:

determining an active expiration time and a breathing period of thesubject;

determining whether a difference between the breathing period and theactive expiration time is greater than or equal to a first function ofthe active expiration time;

responsively to a positive determination, estimating that an inspirationtime of the subject is equal to a second function of the difference; and

responsively to a negative determination, estimating that theinspiration time is equal to a third function of the active expirationtime.

In an embodiment, determining the active expiration time and thebreathing period includes:

generating a raw signal indicative of airflow sounds of the respiration;

analyzing the raw signal to derive an expiratory airflow sound signalfrom the raw signal; and

analyzing the expiratory airflow sound signal to determine the activeexpiration time and the breathing period.

For some applications, determining includes determining whether thedifference between the breathing period and the active expiration timeis greater than or equal to the active expiration time.

For some applications, setting responsively to the positivedetermination includes setting the inspiration time equal to a valuewithin plus or minus 20% of the difference, such as within plus or minus10% of the difference. For some applications, the second functionincludes a function of the difference and a phenomenological constant,and setting responsively to the positive determination includes settingthe inspiration time equal to the second function of the difference andthe phenomenological constant. For some applications, settingresponsively to the positive determination includes determining thephenomenological constant at least in part responsively to at least oneparameter of the respiration.

For some applications, setting responsively to the negativedetermination includes setting the inspiration time equal to a valuewithin plus or minus 20% of the active expiration time, such as withinplus or minus 10% of the active expiration time. For some applications,the third function includes a function of the active expiration time anda phenomenological constant, and setting responsively to the negativedetermination includes setting the inspiration time equal to the thirdfunction of the active inspiration time and the phenomenologicalconstant. For some applications, setting responsively to the negativedetermination includes determining the phenomenological constant atleast in part responsively to at least one parameter of the respiration.

There is also provided, in accordance with an embodiment of the presentinvention, a method for analyzing respiration of a subject, the methodincluding:

generating a raw signal indicative of airflow sounds of the respiration;

defining a detection threshold by:

-   -   digitizing the raw signal to generate a digital signal having        flow values at respective points in time, and buffering the flow        values of the digital signal over a period of time,    -   transforming the buffered flow values into a histogram having a        plurality of bins,    -   designating one of the bins having a greatest number of points        as a maximum bin,    -   selecting two of the bins on opposite sides of the maximum bin,    -   setting a width of a noise band equal to a flow interval between        the two bins, and    -   setting the detection threshold responsively to a flow value of        the maximum bin and the width of the noise band; and

deriving an expiratory airflow sound signal from the raw signal byinterpreting portions of the raw signal having a signal strength greaterthan the detection threshold as the expiratory airflow sound signal.

For some applications, setting the detection threshold includes settingthe detection threshold equal to the flow value of the maximum bin plusa product of a constant and the width of the noise band.

There is yet additionally provided, in accordance with an embodiment ofthe present invention, a method including:

using a non-contact microphone integrated into a ventilator, generatinga signal indicative of airflow sounds of respiration of a subject; and

analyzing the signal to detect active expiration of the subject.

For some applications, the method includes analyzing the activeexpiration to determine whether the ventilator is functioning properly.

For some applications, analyzing the signal includes analyzing alow-frequency component of the signal to detect the active respiration.

For some applications, generating the signal includes generating thesignal during weaning of the subject from ventilation.

There is still additionally provided, in accordance with an embodimentof the present invention, a method including:

using a non-contact microphone integrated into a drug inhaler,generating a signal indicative of airflow sounds of respiration of asubject; and

analyzing the signal to detect active expiration of the subject.

For some applications, the method includes guiding the subject toperform breathing in a plurality of respiration phases determined atleast in part responsively to the detected active expiration.

For some applications, analyzing the signal includes analyzing alow-frequency component of the signal to detect the active respiration.

There is yet additionally provided, in accordance with an embodiment ofthe present invention, a method including:

selecting a subject suffering from insomnia;

using a non-contact microphone, generating a signal indicative ofairflow sounds of respiration of the subject;

analyzing the signal to detect active expiration of the subject; and

treating the insomnia by guiding the subject to perform breathing in aplurality of respiration phases determined at least in part responsivelyto the detected active expiration.

There is still additionally provided, in accordance with an embodimentof the present invention, apparatus for analyzing respiration of asubject, including:

a non-contact microphone, adapted to generate a raw signal representingairflow sounds of the respiration; and

a control unit, adapted to:

analyze the raw signal to determine a first set of one or moreparameters of the respiration, and

apply an algorithm to the first set of parameters to derive a second setof one or more estimated parameters of the respiration that are notgenerally directly measurable in the raw signal.

There is yet additionally provided, in accordance with an embodiment ofthe present invention, apparatus for analyzing respiration of a subject,including:

a microphone, adapted to generate a signal representing airflow soundsof the respiration;

an output generator; and

a control unit, adapted to:

drive the output generator to guide the subject to perform breathing ina plurality of respiration phases,

analyze the signal during the guided respiration phases, and define oneor more parameters of a filter responsively to the analysis, and

filter background noise from the signal using the filter having thedefined parameters.

There is also provided, in accordance with an embodiment of the presentinvention, apparatus for modifying naturally-occurring multi-phasebiorhythmic activity of a subject, the apparatus including:

a sensor, adapted to detect a signal indicative of the multi-phasebiorhythmic activity;

a control unit, adapted to:

-   -   analyze the signal to determine one or more parameters of a        filter,    -   filter background noise from the signal using the filter having        the parameters, and    -   at least in part responsively to the filtered signal, determine        a stimulus input which is operative to change at least one        aspect of the biorhythmic activity of the subject; and

a stimulator, adapted to provide the stimulus input to the subject.

There is further provided, in accordance with an embodiment of thepresent invention, apparatus for analyzing respiration of a subject, theapparatus including a control unit, adapted to:

determine an active expiration time and a breathing period of thesubject,

determine whether a difference between the breathing period and theactive expiration time is greater than or equal to a first function ofthe active expiration time,

responsively to a positive determination, estimate that an inspirationtime of the subject is equal to a second function of the difference, and

responsively to a negative determination, estimate that the inspirationtime is equal to a third function of the active expiration time.

There is still further provided, in accordance with an embodiment of thepresent invention, apparatus for analyzing respiration of a subject, theapparatus including:

a sensor, adapted to generate a raw signal indicative of airflow soundsof the respiration; and

a control unit, adapted to:

define a detection threshold by:

-   -   digitizing the raw signal to generate a digital signal having        flow values at respective points in time, and buffering the flow        values of the digital signal over a period of time,    -   transforming the buffered flow values into a histogram having a        plurality of bins,    -   designating one of the bins having a greatest number of points        as a maximum bin,    -   selecting two of the bins on opposite sides of the maximum bin,    -   setting a width of a noise band equal to a flow interval between        the two bins, and    -   setting the detection threshold responsively to a flow value of        the maximum bin and the width of the noise band, and

derive an expiratory airflow sound signal from the raw signal byinterpreting portions of the raw signal having a signal strength greaterthan the detection threshold as the expiratory airflow sound signal.

There is additionally provided, in accordance with an embodiment of thepresent invention, apparatus for use with a ventilator, the apparatusincluding:

a non-contact microphone, adapted to be integrated into the ventilator,and to generate a signal indicative of airflow sounds of respiration ofa subject; and

a control unit, adapted to analyze the signal to detect activeexpiration of the subject.

There is yet additionally provided, in accordance with an embodiment ofthe present invention, apparatus for use with a drug inhaler, theapparatus including:

a non-contact microphone, adapted to be integrated into the druginhaler, and to generate a signal indicative of airflow sounds ofrespiration of a subject; and

a control unit, adapted to analyze the signal to detect activeexpiration of the subject.

The present invention will be more fully understood from the followingdetailed description of embodiments thereof, taken together with thedrawings, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic pictorial illustration of a microphone-basedbreathing pattern modification system applied to a user, in accordancewith an embodiment of the present invention;

FIG. 2 is a schematic block diagram of the system of FIG. 1, inaccordance with an embodiment of the present invention;

FIG. 3 is a flow chart illustrating a method for determining a breathingpattern from a raw analog signal, in accordance with an embodiment ofthe present invention;

FIGS. 4A and 4B are schematic illustrations of signals analyzed at athreshold calculation step of the method of FIG. 3, in accordance withan embodiment of the present invention;

FIG. 5 is a schematic illustration of a recording of microphone-detectedairflow, recorded using the techniques described herein, and acorresponding recording of chest circumference, recorded usingtechniques known in the art, in accordance with an embodiment of thepresent invention;

FIG. 6 shows an exemplary correlation between a microphone-detectedexpiration time and a belt-determined active expiration time, based onexperimentally-obtained data like (but different from) the data shown inFIG. 5, in accordance with an embodiment of the present invention;

FIG. 7 is a graph showing experimental results measured in accordancewith an embodiment of the present invention;

FIG. 8 is a flow chart illustrating a method for adaptively determiningfiltering frequencies, in accordance with an embodiment of the presentinvention; and

FIGS. 9A and 9B are schematic illustrations of several exemplaryspectra, in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS

FIG. 1 is a schematic pictorial illustration of a microphone-basedbreathing pattern modification system 10 applied to a user 20, inaccordance with an embodiment of the present invention. System 10comprises a microphone 22 and a speaker 24, which typically are standardcomponents of a standard headset or telephone. System 10 furthercomprises a control unit 26, which is coupled to microphone 22 andspeaker 24 via a cable or wirelessly.

FIG. 2 is a schematic block diagram of system 10, in accordance with anembodiment of the present invention. Control unit 26 comprises anamplifier 28, an A/D converter 30, a CPU 32, and a sound synthesizer 34.Sound synthesizer 34 is typically adapted to generate tones, music,and/or synthesized or recorded oral messages.

For some applications, control unit 26 comprises a standard consumerelectronics device, programmed in software to carry out the functionsdescribed herein. For example, control unit 26 may comprise a standardor pocket computer, a personal digital assistant (PDA), a “smart” phone,a telephone, or a cellular phone. Alternatively, at least a portion ofthe functions of control unit 26 are executed on a remote system that isaccessed by a local device over a conventional wireless or wirednetwork. Further alternatively, control unit 26 comprises a customcontrol unit produced to carry out the techniques described herein.

In general, a non-contact standard microphone in a headset designed fora human user can detect the sound of effortless breathing from twosources: (a) sound waves, typically at the frequency range of 500 to5000 Hz, generated inside the body and propagated in the environment,such as speech, and (b) lower-frequency sound waves, which reflectturbulences generated by the airflow in the vicinity of the microphoneduring expiration and sometimes under windy conditions. Standardmicrophones are designed to detect speech, which is categorized in thefirst source (a). Speech is specific to the user's specific anatomy,typically can be detected anywhere in the vicinity of the user, and maybe generated during inspiration and/or expiration. Sound of the secondsource (b) is usually considered to be noise that should be minimized(see, for example, U.S. Pat. No. 4,887,693 to Plice, which isincorporated herein by reference). Such sound is unrelated to the user'sspecific anatomy, is usually restricted to expiration only, andgenerally can be detected only if the microphone is placed in the pathof the airflow.

FIG. 3 is a flow chart illustrating a method for determining a breathingpattern from a raw analog signal, in accordance with an embodiment ofthe present invention. Amplifier 28 amplifies a raw analog signalgenerated by microphone 22, at an amplification step 100, and A/Dconverter 30 digitizes the amplified signal, at a digitization step 102.Control unit 26 buffers the digital signal, at a buffer step 104. Thesize of the buffer is determined responsive to the requirements of thedetection process. For example, if it is desired to perform spectralanalysis as described hereinbelow every t seconds, and if data aresampled at a sampling rate f, then the buffer should typically be largeenough to hold at least t*f data points.

At a filtering step 106, control unit 26 periodically performs spectralanalysis on the buffered data, e.g., every 0.05-1 second (such as every0.1 seconds). Control unit 26 typically performs the spectral analysisusing a discrete Fourier transform (DFT), operating within a range offrequencies between a minimum frequency (fmin) and a maximum frequency(fmax). The minimum and maximum frequencies are typically determinedbased on characteristics of microphone 22 and the specific application.In general, the maximum frequency is set such that the spectral power atfrequencies higher than the maximum frequency is dominated by sounds notassociated with the airflow of expiration. The minimum frequency isgenerally set such that the spectral power at frequencies lower than theminimum frequency is dominated by environmental airflows, e.g., wind.Typically, at filtering step 106, control unit 26 eliminates frequenciesthat are less than a frequency f1 and greater than a frequency f2, wheref1 is greater than minimum frequency fmin and f2 is less than maximumfrequency fmax. For some applications, control unit 26 determines f1 andf2 using adaptive optimization techniques described hereinbelow withreference to FIGS. 8, 9A, and 9B. Alternatively, f1 and f2 arepre-selected (in which case the filtering is not adaptive). Typically,f1 is between about 30 and about 50 Hz, and f2 is between about 100 andabout 200 Hz.

At a spectrum integration step 108, control unit 26 integrates the powerof the spectrum, and, typically, smoothes the signal using amoving-average calculation, at a moving average calculation step 110.The resulting temporal (i.e., non-spectral) signal is indicative of themicrophone-detected expiratory airflow. The control unit (a) buffersthis signal at a buffer step 112, and (b) analyzes this signal at anexpiratory airflow detection step 114, both of which are describedhereinbelow.

At buffer step 112, control unit 26 buffers the temporal signalsdetected during the N most recent breathing periods. N is determined bythe pattern parameters to assure that the data distribution issufficient for threshold determination. For example, N may be selectedto include at least five detected breathing periods, as shownhereinbelow in FIG. 4A.

At a threshold calculation step 116, using data stored at buffer step112, control unit 26 calculates a detection threshold at selected timeintervals, typically using the techniques described hereinbelow withreference to FIGS. 4A and 4B. For some applications, the detectionthreshold is set equal to the greater of (a) the threshold determinedusing the techniques described hereinbelow with reference to FIGS. 4Aand 4B, and (b) a secondary threshold determined at step 118, asdescribed hereinbelow. At expiratory airflow detection step 114, controlunit 26 detects the onset and cessation of expiratory-related airflow byanalyzing the microphone-detected airflow with respect to the detectionthreshold. The control unit interprets airflow having a signal strengthgreater than the detection threshold as expiration, and airflow having asignal strength less than the detection threshold as background noise.

For some applications, at expiratory airflow detection step 114, controlunit 26 generates a real-time indication for user 20, indicating thatexpiration has been detected. For some applications, this indicationhelps user 20 optimize the positioning of microphone 22. Alternativelyor additionally, if no respiration signal has been detected over acertain period of time, control unit 26 notifies user 20 that microphone22 should be repositioned.

At a pattern determination step 118, control unit 26 determinesbreathing pattern parameters, such as described hereinbelow withreference to FIGS. 5, 6, and 7. In order to determine these parameters,it is desirable to have a sufficiently large number of recent breathingdata points to process, and these are stored at buffer step 112. Forexample, if it is desired to have breathing data representative of fivebreaths, and if the most recent five breaths occurred over a 45 secondperiod and were sampled at 10 Hz, then the buffer should typicallycontain at least 450 points. (Data processing techniques as are known inthe art may be applied to reduce this number.)

In an embodiment, use of a secondary threshold at threshold calculationstep 116, described hereinabove, reduces or eliminates erratic peaksthat cannot be associated with functional breathing according topredetermined rules. For example, the secondary threshold may be used toidentify erratic peaks in certain recorded signals that are associatedwith the user: (a) talking (rather than breathing), or (b) exhaling dueto a change in posture, thereby forcing air out of his lungs (not aspart of a functional breathing cycle).

As appropriate, control unit 26 may define and utilize one or moresecondary thresholds by: (a) defining secondary thresholds responsive toone or more values associated with the previous 1-5 breaths (e.g.,setting a secondary threshold to be n times larger than the averagerespiration volume associated with the breaths), (b) comparing thecorresponding parameters for the currently-recorded breathing data withthe secondary thresholds, and (c) accepting the currently-recordedbreathing data as being indicative of actual functional breathing if thecorresponding parameters do not pass the secondary thresholds. For someapplications, control unit 26 determines a secondary threshold value atpattern determination step 118 (for example by taking a percentage,e.g., 20%, of the most recent average flow amplitude, as reflected inthe breathing pattern and/or a series of breaths).

Reference is now made to FIGS. 4A and 4B, which are schematicillustrations of signals analyzed at threshold calculation step 116(FIG. 3), in accordance with an embodiment of the present invention.FIG. 4A shows a trace 160 of the digitized microphone signal stored atbuffer step 112 over a 20-second period. Trace 160 is transformed into ahistogram 170, shown in FIG. 4B. Typically, this transformation isperformed by setting the bin width of histogram 170 equal to thesmallest value that results in at least one bin having at least athreshold number of points n, e.g., about 20 data points. Typically, thebin width is set such that a minimum or near-minimum number of the binshave at least the threshold number of points.

According to a first method for setting the bin width, control unit 26sets an initial, transitional bin width to a low value, typically 1. Thecontrol unit counts the number of points in each resulting bin. If allthe bins have fewer than n points, the control unit increments thetransitional bin width, typically by 1, and the method loops back to theprevious counting step. On the other hand, if any of the bins has atleast n points, the control unit designates the bin having the greatestnumber of points M as a maximum bin 172.

According to a second method for setting the bin width, control unit 26applies a successive-approximation procedure to the data of trace 160.The control unit creates an initial, transitional histogram, having asingle bin having a flow data range between 0 and F, where F is themaximum flow value of trace 160. The control unit divides the flow datarange into two equal flow intervals: (1) from 0 to F/2 and (2) from F/2to F. The control unit selects the flow interval containing the largernumber of points, and compares the number of points in the selected flowinterval to n. If the number of points in the selected flow interval isgreater than or equal to n, the method returns to the division stepabove, at which the selected flow interval is divided into two flowintervals. On the other hand, if the number of points in the selectedflow interval is less than n, the control unit typically reverses themost recent division, resulting in a flow interval having M points,where M is greater than or equal to n. The control unit uses theresulting flow interval as the bin width of histogram 170. The controlunit designates the bin of histogram 170 having the greatest number ofpoints M as maximum bin 172.

Maximum bin 172 has a flow value (y-axis) of B. Flow value B typicallycorresponds to the average flow of the background noise of the signal.Typically, control unit 26 selects two bins 176 and 178 on oppositesides of maximum bin 172 having numbers of points closest to M/2, andsets a width W of a noise band 180 equal to the flow interval betweenbins 176 and 178. (Instead of using M/2, as described, for someapplications a value of M/k is used, where k is typically between 1.5and 4.) The control unit typically sets a detection threshold 182 equalto (a) B plus (b) the product of W and a constant, for example, equal toB plus an integer multiple of W, e.g., B+2W: Typically, control unit 26determines detection threshold 182 substantially continuously, resultingin an adaptive detection process.

Reference is now made to FIG. 5, which is a schematic illustration of arecording 250 of microphone-detected airflow, recorded using thetechniques described herein, and a corresponding recording 252 of chestcircumference, recorded using techniques known in the art, in accordancewith an embodiment of the present invention. Microphone-detectedrecording 250 and chest circumference recording 252 were recordedsimultaneously in the same user. Microphone-detected recording 250 wasfiltered using frequencies f1 and f2 of 30 Hz and 150 Hz, respectively,at filtering step 106, described hereinabove with reference to FIG. 3.Chest circumference recording 252 was recorded using a belt-type sensorsimilar to the belt-type sensor described in the above-mentioned U.S.Pat. Nos. 5,423,328 and 6,090,037 to Gavish. Because such belt-typesensors produce highly accurate measurements of breathing phases, chestcircumference recording 252 serves as a control for determining theaccuracy of microphone-based breathing phase determinations using thetechniques described herein.

Chest circumference recording 252 clearly shows all phases of normalbreathing: inspiration and expiration, including active expiration and apost-expiratory pause. Chest circumference recording 252 is analyzedusing min-max analysis to derive inspiration time T_(in), breathingperiod T, expiration time T_(ex)=T−T_(in), the active expiration timeT_(ax), and breathing amplitude A, for example as described in theabove-mentioned U.S. Pat. No. 5,800,337.

In contrast, microphone-detected recording 250 shows only the activeexpiration phase of breathing. At least an estimation of inspirationtime is necessary for certain applications (including breathing patternmodification by generating inspiration- and expiration-related guidingtones, as described, for example, in the above-referenced patents andpatent application publications to Gavish and Gavish et al.).

Reference is made to FIG. 6, which shows an exemplary correlationbetween microphone-detected expiration time T_(f) and belt-determinedactive expiration time T_(ax), using the data shown in FIG. 5, inaccordance with an embodiment of the present invention. The correlationbetween T_(f) and T_(ax), as determined using linear regression, isr=0.98 with slope 0.97±0.05 (expected 1) and an intercept of nearly zero(expected 0). The accuracy in time is 0.22 seconds, where 0.14 secondsis the statistical error in measuring the time interval under a 10 Hzsampling rate. A similar correlation was found between themicrophone-detected and belt-determined breathing periods (data notshown). These data indicate that the microphone-detected parameters arecomparable to those detected by a belt-type sensor.

Reference is again made to FIG. 5. In an embodiment of the presentinvention, a method is provided for estimating inspiration time T_(inf)using microphone-detected active expiration time T_(f) and breathingperiod T (time between successive breaths). This method is typicallyused for carrying out pattern determination step 118, describedhereinabove with reference to FIG. 3. The airflow signal reflected inrecording 250 generally corresponds to the time derivative (withinverted sign) of the chest-circumference recording 252 duringexpiration, because airflow is the time derivative of lung volume asindicated by chest circumference.

The inventors have observed that effortless breathing generally can becharacterized by one of two patterns:

-   -   a first pattern 260, in which the end of active expiration        T_(ax) occurs generally at the same time as the beginning of the        subsequent inspiration T_(in). For this pattern, T_(inf) is        estimated using the following formula:        T _(inf) =T−T _(f)  (1)    -   For some applications, T_(inf) is set to a function of T−T_(f).        For example, T_(inf) may be set to the product of T−T_(f) and a        constant between about 0.8 and about 1.2, such as between about        0.95 and about 1.05. Alternatively, T_(inf) may be set to the        sum of T−T_(f) and the value, wherein the value is positive or        negative. For some applications, T_(inf) is set to be equal to        T−T_(f) within plus or minus 20%, such as within plus or minus        10%. For some applications, T_(inf) is set to a phenomenological        function of T−T_(f), or of other respiration-related parameters        measured using techniques described herein. For some        applications, one or more constants of the phenomenological        function is determined at least in part responsively to at least        one parameter of the respiration.    -   a second pattern 262, in which the end of active expiration        T_(ax) is followed by a phase Tp with no chest movement, which        is followed by the beginning of the subsequent inspiration        T_(in). For this pattern, T_(inf) is typically estimated using        the following formula:        T_(inf)=T_(f)  (2)    -   For some applications, T_(inf) is set to a function of T_(f).        For example, T_(inf) may be set to the product of T_(f) and a        constant between about 0.8 and about 1.2, such as between about        0.95 and about 1.05. Alternatively, T_(inf) may be set to the        sum of T_(f) and the value, wherein the value is positive or        negative. For some applications, T_(inf) is set to be equal to        T_(f) within plus or minus 20%, such as within plus or minus        10%. For some applications, T_(inf) is set to a phenomenological        function of T_(f), or of other respiration-related parameters        measured using techniques described herein. For some        applications, one or more constants of the phenomenological        function is determined at least in part responsively to at least        one parameter of the respiration.

The inventors have observed that in first pattern 260, active expirationtime T_(ax) generally has a duration greater than the duration ofinspiration time T_(in), sometimes up to 5 times greater. In contrast,in second pattern 262, T_(ax) generally has a similar duration to thatof T_(in). The inventors have observed that first pattern 260 is usuallyobtained when a user is performing an action such as pursed lipsbreathing, i.e., artificially prolonging expiration by narrowing the gapbetween the lips, which is a known natural therapeutic maneuver. Secondpattern 262 reflects a general natural tendency to match the flow at thebeginning of inspiration and expiration.

Formulas (1) and (2) can be combined and represented by the followingalgorithm:If T−T _(f) ≧T _(f)then T _(inf) =T−T _(f)else T_(inf)=T_(f)  (3)As mentioned above, for some applications, T_(inf) is set to a functionof T−T_(f) or T_(f), depending on the evaluation of the condition.Alternatively or additionally, the condition evaluated by the algorithmis: If T−T_(f)≧a function of T_(f).

FIG. 7 is a graph showing experimental results measured in accordancewith an embodiment of the present invention. The graph showsmicrophone-detected T_(inf) calculated using algorithm (3) (y-axis) vs.T_(in) measured with a belt-type sensor (x-axis), with the data pointsrandomly selected from data of 10 different users (3 breaths for eachuser). The correlation between T_(inf) and T_(in), as determined usinglinear regression, is r=0.82 with slope 0.97±0.13 (expected 1) and anintercept of nearly zero (expected 0). The accuracy in time is 0.54seconds, where 0.14 seconds is the statistical error in measuring thetime interval under a 10 Hz sampling rate. This error represents about25% of the average inspiration time T_(in) of 2.1 seconds, and 7.8% ofthe average breathing period T of 6.9 seconds. These results aregenerally sufficiently accurate for the purpose of modifying breathingpatterns by generating guiding tones. The nearly-unity slope between theparameters found using algorithm (3) and the belt-type sensor suggeststhat the differences are attributable to random data scattering. In anembodiment (typically during a breathing pattern modificationprocedure), the scattering is reduced to an acceptable level byaveraging the value of T measured during multiple breathing cycles,and/or by averaging the value of T_(inf) measured during multiplebreathing cycles.

In an embodiment of the present invention, additional breathingparameters are derived from the microphone-detected airflow. The airflowcan be expressed as Y(i,t), where Y is the magnitude of airflow, i isbreath number, and t is the time extending from the beginning of breathi until the end of breath i. The additional breathing parametersinclude:

-   -   breathing amplitude, which is represented by the integrated        airflow for a single breath, which is given by:

$\begin{matrix}{{{Af}(i)} = {\sum\limits_{t}{Y\left( {i,t} \right)}}} & (4)\end{matrix}$

-   -   The integrated airflow represents the depth of breathing for the        i^(th) breath. (Alternatively, breathing amplitude is        represented by the maximum value of Y(i,t) over a suitable        interval.)    -   the geometrical properties of the airflow pattern by means of        moments, which is given by:

$\begin{matrix}{{\sum\limits_{t}\left( {{Y\left( {i,t} \right)} \cdot \left( {t - \left\langle {ti} \right\rangle} \right)^{n}} \right)}{{where}\text{:}}{{{(a)\left\langle {ti} \right\rangle} = \frac{\sum\limits_{t}\left( {{Y\left( {i,t} \right)} \cdot t} \right)}{\sum\limits_{t}{Y\left( {i,t} \right)}}},{and}}} & (5)\end{matrix}$

-   -   -   (b) n is the order of the moment.

For example, if n=0, 1, 2, or 3, the sum corresponds, respectively, to(a) integrated area, (b) zero by definition, (c) variance of the airflowpattern, and (d) asymmetry of the airflow pattern.

For some applications, breathing amplitude is determined, such asdescribed above, and at least one additional parameter is derived fromthe breathing amplitude. For example, the additional parameter may be:(a) a measure of ventilation (amount of air per unit time), such as aproduct of the breathing amplitude and the respiration period, or (b) ameasure of breathing irregularity, such as relative fluctuations inbreathing amplitude (e.g., the standard deviation of the n lastbreathing amplitude values, divided by the mean of the breathingamplitude). Breathing irregularity generally increases during stress andsome diseases. For some applications, other characteristics of theairflow are detected, such as by fitting mathematical models having aphysiological rationale, such as:

-   -   a square pulse that corresponds to uniform expiration, which        characterizes pursed lips breathing;    -   a fast-rising but gradually decreasing flow followed by a        post-expiratory pause, which characterizes relaxed breathing; or    -   an exponential-decay that characterizes passive elastic recoil        of the lungs against airways resistance.

For some applications, the airflow is analyzed to detect acharacteristic of breathing with effort. For some applications, theairflow is analyzed to detect a characteristic of breathing during whichthe lungs undergo a functional change, such as when the subject suffersfrom asthma, emphysema, or another condition in which small airwayscollapse during expiration (which cannot be characterized as a simpleelastic recoil).

FIG. 8 is a flow chart illustrating a method for adaptively determiningfiltering frequencies, in accordance with an embodiment of the presentinvention. For some applications, control unit 26 uses this method todetermine filtering frequencies f1 and f2, described hereinabove withreference to filtering step 106 of FIG. 3, when system 10 is used forbreathing pattern modification. Typically, when system 10 is turned on,or when airflow is undetected for a predetermined amount of time,control unit 26 automatically enters a metronome mode of operation. Inthis mode of operation, CPU 32 alternatingly generates inspiration andexpiration phase indicators, at a phase indicators generation step 300.For example, CPU 32 may generate each inspiration phase indicator forbetween about 1 and about 5 seconds, e.g., about 2 seconds, and eachexpiration phase indicator for between about 2 and about 10 seconds,e.g., about 4 seconds. CPU 32 typically generates each of the phasesbetween about 5 and about 10 times.

Responsively to the phase indicators, sound synthesizer 34 generatestones and/or oral messages that instruct user 20 to synchronizebreathing with the inspiration and expiration phases, at a guide userstep 302. Microphone 22 detects the resulting user-generated airflow, atan airflow detection step 304. Amplifier 28 amplifies the signal, andA/D converter 30 digitizes the amplified signal, at an amplification anddigitization step 306. At a buffer and mark step 308, control unit 26buffers the digital signal, and marks the buffered signal with the phaseindicator generated when the signal was recorded.

At a filtering step 310, control unit 26 periodically performs spectralanalysis on the buffered data, e.g., every 0.1 seconds. Control unit 26typically performs the spectral analysis using a DFT, operating within arange of frequencies between fmin and fmax, as described hereinabovewith reference to filtering step 106 of FIG. 3. Control unit 26eliminates frequencies that are less than fmin and greater than fmax. Ata spectrum integration step 312, control unit 26 integrates the power ofseparate spectrums for the signals stored during the inspiration phaseand the expiration phase, respectively, and typically smoothes thesignals using a moving-average calculation, at a moving averagecalculation step 314. The resulting inspiration and expiration spectraare separately buffered at an inspiration buffer step 316 and anexpiration buffer step 318, respectively.

Reference is made to FIGS. 9A and 9B, which are schematic illustrationsof several exemplary spectra, in accordance with an embodiment of thepresent invention. FIG. 9A shows an exemplary inspiration spectrum 350,and an exemplary expiration spectrum 352. At a spectra subtraction step320 (FIG. 8), control unit 26 subtracts inspiration spectrum 350 fromexpiration spectrum 352 to obtain a net spectrum 354, shown in FIG. 9B,which is associated with expiratory airflow sounds. Control unit 26typically averages net spectrum 352 over a selected number of guidedbreaths. At a parameter calculation step 322, control unit 26 calculatesparameters of net spectrum 352, including f1 and f2, describedhereinabove with reference to filtering step 106 of FIG. 3. Theresulting parameters are used at filtering step 106, as describedhereinabove with reference to FIG. 3. For some applications, f1 is setto a value such that the area under net spectrum 354 to the left of f1is r1% (e.g., 10%) of the total area under net spectrum 354, and f2 isset to a value such that the area under net spectrum 354 to the right off2 is r2% (e.g., 10%) of the total area under net spectrum 354. Asappropriate, r1 may be equal or not equal to r2. Alternatively oradditionally, control unit 26 derives other parameters of net spectrum354 that are used for filtering at filtering step 106 of FIG. 3.

Reference is again made to FIG. 1. In an embodiment of the presentinvention, microphone 22 is integrated into a medical device in fluidcommunication (e.g., via air or another gas) with respiration-relatedairflow of user 20. For example, the medical device may comprise abreathing mask or a tube, such as a tracheotomy tube. For someapplications, the breathing mask or tube are components of a ventilatorthat applies positive pressure to the lungs of user 20. The techniquesdescribed herein are used to detect proper performance of theventilator, typically by detecting active expiration by the user. Activeexpiration is typically measured by detecting low-frequency soundsindicative of expiratory airflow, rather than by detecting sounds ofbreathing. (In such subject, sounds of breathing often do not correlatewith expiration, because the sounds of breathing are often affected byvarious constrictions in the subject's airways. However, low-frequencysounds indicative of expiratory airflow are not generally affected bysuch constrictions.) For some applications, the techniques describedherein are used for non-contact monitoring of breathing during weaningfrom ventilation.

In an embodiment of the present invention, the techniques describedherein are used for non-contact monitoring of breathing during use of adrug inhaler by the subject. Typically, microphone 22, and, optionally,other components of system 10, are integrated into the drug inhaler. Forsome applications, such non-contact monitoring of breathing is combinedwith techniques for modifying breathing activity of the subject, such asthose described in the above-mentioned patent and patent applicationpublications to Gavish and Gavish et al.

In an embodiment of the present invention, the techniques describedherein and/or in the above-mentioned patent and patent applicationpublications to Gavish and Gavish et al. are used to treat a subjectsuffering from insomnia. Insomnia is sometimes caused by disorderedbreathing, such as fast and shallow breathing. For some applications,insomnia is treated using techniques described herein for detecting andmonitoring breathing, in combination with techniques for modifyingrespiration-related biorhythmic activity of the subject described in theabove-mentioned patent and patent application publications to Gavish andGavish et al.

In an embodiment of the present invention, the breathing monitoringtechniques described herein are used for detecting sleep-disorderedbreathing, such as sleep-disordered breathing associated with sleepapnea or sudden infant death syndrome (SIDS). Typically,breath-by-breath airflow during exhalation is monitored. For someapplications, such non-contact monitoring of breathing is combined withtechniques for modifying breathing activity of the subject, such asthose described in the above-mentioned patent and patent applicationpublications to Gavish and Gavish et al.

In an embodiment of the present invention, techniques described hereinare used in combination with techniques for modifying biorhythmicactivity of user 20. Typically, the biorhythmic activity includesrespiration. The user is guided to inhale for a certain period of time,exhale for a certain period of time, and, optionally, to hold his breathfor a certain period of time.

In an embodiment of the present invention, a method is provided formodifying naturally-occurring multi-phase biorhythmic activity of asubject, such as respiration of the subject. The method comprisesdetecting a signal indicative of the multi-phase biorhythmic activity,and analyzing the signal to determine one or more parameters of afilter. Background noise is filtered from the signal using the filter.At least in part responsively to the filtered signal, a stimulus input,such as an audio and/or visual stimulus input, is determined which isoperative to change at least one aspect of the biorhythmic activity ofthe subject, and the stimulus input is provided to the subject. For someapplications, the background noise is indicative of secondarybiorhythmic activity different from the multi-phase biorhythmicactivity, and the secondary biorhythmic activity-related backgroundnoise is filtered from the signal.

For some applications, the background noise is filtered from the signalby frequency filtering the signal. Alternatively or additionally, thesignal is filtered by performing spectral analysis on the signal toproduce a frequency spectrum. For example, the frequency spectrum may befrequency filtered. Further alternatively, non-frequency spectralanalysis is performed on the signal in order to classify the signalaccording to one or more variables.

For some applications, the background noise is filtered to removenon-frequency-related noise from the signal, typically to eliminate aportion of the signal that is not relevant to determining to thestimulus input. For example, a breathing-related signal (e.g., monitoredusing a belt-type sensor) may include a heartbeat-related componentwhich is noise with respect to the respiration-related component of thesignal. This heartbeat-related component is eliminated from the signal,typically using non-frequency-related filtering, such as by identifyingsmall peaks characteristic of the heartbeats and removing them from thesignal.

As appropriate, techniques described herein are practiced in conjunctionwith techniques described in the above-mentioned patents and patentapplication publications to Gavish and Gavish et al.

Although metronome stimuli and/or other instructions have generally beendescribed herein as including audio tones and/or oral messages, suchstimuli and instructions may also take additional forms, such as visualdisplay images, e.g., text messages (e.g., “inhale” and “exhale”),and/or dynamically changing graphical features, e.g., color and form. Insuch cases, sound synthesizer 34 and speaker 24 are replaced with anappropriate output generator. Alternatively, sound synthesizer 34generates oral guiding messages only, rather than tones.

It will be appreciated by persons skilled in the art that the presentinvention is not limited to what has been particularly shown anddescribed hereinabove. Rather, the scope of the present inventionincludes both combinations and subcombinations of the various featuresdescribed hereinabove, as well as variations and modifications thereofthat are not in the prior art, which would occur to persons skilled inthe art upon reading the foregoing description.

The invention claimed is:
 1. A method for modifying naturally-occurringmulti-phase biorhythmic activity of a subject, the method comprising:detecting, using a sensor, a signal indicative of the multi-phasebiorhythmic activity; analyzing, using a control unit, the signal todetermine one or more parameters of a filter; filtering, using thecontrol unit, background noise from the signal using the filter havingthe parameters; at least in part responsively to the filtered signal,determining, using the control unit, a stimulus input which is operativeto change at least one aspect of the biorhythmic activity of thesubject; and providing the stimulus input to the subject, using astimulator, wherein the multi-phase biorhythmic activity includesrespiration of the subject, and wherein detecting the signal comprisesdetecting the signal indicative of the respiration.
 2. The methodaccording to claim 1, wherein filtering the background noise comprisesfrequency filtering the signal.
 3. The method according to claim 1,wherein filtering the background noise comprises performingnon-frequency spectral analysis on the signal in order to classify thesignal according to one or more variables.
 4. The method according toclaim 1, wherein the background noise is indicative of secondarybiorhythmic activity different from the multi-phase biorhythmicactivity, and wherein filtering the background noise from the signalcomprises filtering the secondary biorhythmic activity-relatedbackground noise from the signal.
 5. The method according to claim 1,wherein the background noise includes a heartbeat-related component ofthe signal, and wherein filtering the background noise from the signalcomprises filtering the heartbeat-related component from the signal. 6.The method according to claim 1, wherein filtering the background noisecomprises removing non-frequency-related noise from the signal.
 7. Amethod for modifying naturally-occurring multi-phase biorhythmicactivity of a subject, the method comprising: detecting, using a sensor,a signal indicative of the multi-phase biorhythmic activity; analyzing,using a control unit, the signal to determine one or more parameters ofa filter; filtering, using the control unit, background noise from thesignal using the filter having the parameters; at least in partresponsively to the filtered signal, determining, using the controlunit, a stimulus input which is operative to change at least one aspectof the biorhythmic activity of the subject; and providing the stimulusinput to the subject, using a stimulator, wherein filtering thebackground noise comprises performing spectral analysis on the signal toproduce a frequency spectrum.
 8. The method according to claim 7,wherein performing the spectral analysis comprises frequency filteringthe frequency spectrum.
 9. The method according to claim 6, wherein thenon-frequency-related noise includes a heartbeat-related component ofthe signal, and wherein removing the non-frequency-related noisecomprises removing the heartbeat-related component of the signal fromthe signal.
 10. Apparatus for modifying naturally-occurring multi-phasebiorhythmic activity of a subject, the apparatus comprising: a sensor,adapted to detect a signal indicative of the multi-phase biorhythmicactivity; a control unit, adapted to: analyze the signal to determineone or more parameters of a filter, filter background noise from thesignal using the filter having the parameters, and at least in partresponsively to the filtered signal, determine a stimulus input which isoperative to change at least one aspect of the biorhythmic activity ofthe subject; and a stimulator, adapted to provide the stimulus input tothe subject, wherein the multi-phase biorhythmic activity includesrespiration of the subject, and wherein the sensor is adapted to detectthe signal indicative of the respiration.
 11. The apparatus according toclaim 10, wherein the control unit is adapted to filter the backgroundnoise by frequency filtering the signal.
 12. The apparatus according toclaim 10, wherein the control unit is adapted to filter the backgroundnoise by performing non-frequency spectral analysis on the signal inorder to classify the signal according to one or more variables.
 13. Theapparatus according to claim 10, wherein the background noise isindicative of secondary biorhythmic activity different from themulti-phase biorhythmic activity, and wherein the control unit isadapted to filter the secondary biorhythmic activity-related backgroundnoise from the signal.
 14. The apparatus according to claim 10, whereinthe background noise includes a heartbeat-related component of thesignal, and wherein the control unit is adapted to filter theheartbeat-related component from the signal.
 15. The apparatus accordingto claim 10, wherein the control unit is adapted to filter thebackground noise by removing non-frequency-related noise from thesignal.
 16. Apparatus for modifying naturally-occurring multi-phasebiorhythmic activity of a subject, the apparatus comprising: a sensor,adapted to detect a signal indicative of the multi-phase biorhythmicactivity; a control unit, adapted to: analyze the signal to determineone or more parameters of a filter, filter background noise from thesignal using the filter having the parameters, and at least in partresponsively to the filtered signal, determine a stimulus input which isoperative to change at least one aspect of the biorhythmic activity ofthe subject; and a stimulator, adapted to provide the stimulus input tothe subject, wherein the control unit is adapted to filter thebackground noise by performing spectral analysis on the signal toproduce a frequency spectrum.
 17. The apparatus according to claim 16,wherein the control unit is adapted to frequency filter the frequencyspectrum.
 18. The apparatus according to claim 15, wherein thenon-frequency-related noise includes a heartbeat-related component ofthe signal, and wherein the control unit is adapted to remove theheartbeat-related component of the signal from the signal.